saikat Gochhait
@siu.edu.in
Assistant Professor Academic Level 12 7th Pay CPC
Symbiosis International Deemed University
Dr. Saikat Gochhait teaches at Symbiosis Institute of Digital & Telecom Management, Symbiosis International Deemed University Pune, India and Neurosciences Research Institute-Samara State Medical University, Russia. He is Ph.D and Post-Doctoral Fellow from the UEx, Spain and National Dong Hwa University, Taiwan. He was Awarded DITA and MOFA Fellowship in 2017 and 2018. His research publication with foreign authors is indexed in Scopus, ABDC, and Web of Science. He is a Senior IEEE member.
EDUCATION
Post Doctoral Fellow - Uex, Spain
Post Doctoral Fellow - National Dong Hwa University, Taiwan
PhD - Sambalpur University
RESEARCH INTERESTS
Technology Management
Marketing
Healthcare
Entrepreneurship
FUTURE PROJECTS
Neurosciences
NeuroMarketing
Applications Invited
Collaborators
Entreprenuership
Women Entrepreneurs
Applications Invited
Collaborators
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Amitesh Prakash, Saikat Gochhait, Prabakaran Raghavendran, and Tharmalingam Gunasekar
IGI Global
Modern simulation models of virtual reality (VR) and augmented reality (AR) are, at present, enhancing medical education. Users can engage structures in real-time 3D interaction using virtual reality. Advanced technologies in haptics, display systems, and motion detection help the user to achieve an experience of realism with interactive features; hence VR is best suited for practical procedures training. As such, applications of VR are found more in surgeries and other interventional procedures. The application of AR allows for the modification or augmentation of the physical environment by combining virtual data and structures with physical objects. It seems useful to have AR applications as an integral part of our knowledge concerning physiological and anatomical processes. Numerous VR and AR applications using various hardware platforms and in diverse settings have been the subject of experiments aiming to prove their realism and didactic value. Some history of VR AR in medicine can be found in this chapter, and some guide ideals and norms rule them.
Prakash Chand Thakur, Dinesh Thakur, Tharmalingam Gunasekar, Prabakaran Raghavendran, and Saikat Gochhait
IGI Global
This paper presents a cryptographic framework that incorporates the Anuj Transform and the congruence modulo operator to improve data security and allow for efficient information retrieval. The methodology, based on the mathematical properties of the Anuj Transform and its inverse, is used in designing strong encryption and decryption techniques. The additional security of encrypted messages is assured by the incorporation of the congruence modulo operator. Comprehensive analyses are carried out through graphs and evaluations over the principal parameters: encryption precision, computing speed, resistance, scalability. The outcome shows how well the Anuj Transform coupled with the congruence modulo operator can really help to face modern problems within cryptography.
Saikat Gochhait
IGI Global
Although online social platforms are vulnerable to private information leakage, third parties can still do want with your data easily and consent. With Indeed the rapid spread of information today and changed role for social media, people more commonly worry over privacy. India's Digital Personal Data Protection Act 2023 seeks to cope with this risk by strengthening data protection. The legal framework must evolve constantly to guarantee the privacy and dignity of its recipients, permitting properly informed control over personal information in a world increasingly digital all the time.
Prabakaran Raghavendran, Tharmalingam Gunasekar, and Saikat Gochhait
IEEE
This study examines the emergent interest in accurate Solana price predictions among depositors, buyers, and governmental bodies. Solana, a groundbreaking cryptocurrency known for its reorganized nature, has appealed substantial responsiveness. Applying progressive artificial neural networks (ANN), we aim to projection Solana prices by leveraging their capacity to understand the intricate and impulsive outlines typical of cryptocurrency markets. Our pioneering line of attack encompasses exploring diverse lag conformations over specific time intervals to optimize forecast accuracy and timeliness. Through rigorous validation, focusing on root mean square error as a key performance metric, our ANN model dependably outclasses traditional prediction methods. These findings offer valuable insights for individuals, industries, and governmental bodies directing the intricacies of the cryptocurrency landscape. Furthermore, we introduce an algorithm and provide Python code to determine the execution of our approach for forecasting Solana prices.
Prabakaran Raghavendran, Tharmalingam Gunasekar, and Saikat Gochhait
European Alliance for Innovation n.o.
This paper examines various types of fractional differential equations using fractional calculus methods. It extends the classical Frobenius method and introduces key theorems that apply the Ramadan Group transform and other techniques. Additionally, the research incorporates machine learning, specifically neural networks, to solve these equations. The paper demonstrates that machine learning can enhance the solution process through data generation, model design, and optimization. Examples provided illustrate how combining traditional methods with machine learning can effectively solve fractional differential equations.
M. Vijai, T. Ananth Kumar, P. Kanimozhi, and Saikat Gochhait
IEEE
Lane detection and tracking are crucial for modern vehicle navigation systems, especially for ADAS and autonomous vehicles. Traditional methods often fail under adverse conditions such as poor lighting, bad weather, and inconsistent road markings. This paper presents a novel approach using YOLOv5, an advanced object detection model known for its real-time performance and accuracy, to detect lane boundaries directly from images. We improved its robustness in challenging scenarios by adapting YOLOv5 for lane detection and introducing innovative post-processing techniques. These techniques include refining lane predictions, handling occlusions, and reducing noise. Extensive experiments on datasets from various conditions (daytime, nighttime, and adverse weather) show that our method outperforms existing approaches. The proposed YOLOv5-based system offers a promising solution for real-world driving challenges, enhancing the precision and dependability of lane recognition and tracking and positively impacting road safety and autonomous vehicle technologies.
Rushali Garg, Anuradha S. Kanade, Prabha Kiran, and Saikat Gochhait
Springer Nature Singapore
Palla Manoj Babu, Ashish Kumar, P. Venkata Subbaiah, V. Mouneswari, Prabha Kiran, and Saikat Gochhait
Springer Nature Singapore
Saikat Gochhait, Deepak K. Sharma, and Mrinal Bachute
University of Basrah - College of Engineering
Accurate long-term load forecasting (LTLF) is crucial for smart grid operations, but existing CNN-based methods face challenges in extracting essential features from electricity load data, resulting in diminished forecasting performance. To overcome this limitation, we propose a novel ensemble model that integrates a feature extraction module, densely connected residual block (DCRB), long short-term memory layer (LSTM), and ensemble thinking. The feature extraction module captures the randomness and trends in climate data, enhancing the accuracy of load data analysis. Leveraging the DCRB, our model demonstrates superior performance by extracting features from multi-scale input data, surpassing conventional CNN-based models. We evaluate our model using hourly load data from Odisha and day-wise data from Delhi, and the experimental results exhibit low root mean square error (RMSE) values of 0.952 and 0.864 for Odisha and Delhi, respectively. This research contributes to a comparative long-term electricity forecasting analysis, showcasing the efficiency of our proposed model in power system management. Moreover, the model holds the potential to sup-port decision making processes, making it a valuable tool for stakeholders in the electricity sector.
Priyank Kumar Singh, Mohit Yadav, Saikat Gochhait, and Puwakpitiyage Gayan Dhanushka Wijethilaka
IGI Global
The burgeoning field of AI-powered healthcare prognosis offers immense potential, but traditional data center infrastructure creates a significant environmental footprint. This chapter advocates for energy-efficient AI algorithms and hardware alongside renewable energy integration (solar, wind) to minimize reliance on fossil fuels. Robust security measures and privacy-preserving techniques are crucial to protect sensitive patient data used in AI models. Finally, scalable cloud-based infrastructure with containerization and auto-scaling ensures efficient handling of growing data volumes and user demands. By prioritizing these principles, we can create a sustainable and secure future where AI empowers healthcare prognosis, improving patient outcomes for generations to come.
Shashank Mittal, Priyank Kumar Singh, Saikat Gochhait, and Shubham Kumar
IGI Global
Accurate disease prognosis is crucial for improved healthcare outcomes. Artificial intelligence (AI) offers immense potential in this domain, but traditional “black-box” models lack interpretability. This chapter explores the integration of Explainable AI (XAI) with Green AI, a resource-efficient and sustainable approach to AI development. They discuss how XAI can enhance trust in Green AI models for disease prognosis, mitigate potential biases, and promote responsible AI development. They highlight the challenges of balancing interpretability with efficiency and propose future research directions to unlock the full potential of XAI for Green AI-powered disease prognosis. This approach has the potential to revolutionize healthcare by providing accurate, transparent, and environmentally friendly tools for early disease detection and improved patient outcomes.
Priyank Kumar Singh, Mohit Yadav, Saikat Gochhait, and P. G. S. Amila Jayarathne
IGI Global
In this chapter, the authors aim to discuss the significance of integrating AI prediction and green computing in the healthcare field to improve disease diagnosis, treatment, and patient care and minimise the adverse effects on the environment. The methodology employed is the systematic literature review (SLR) approach. The results show that combining green practices with AI prediction enhances the effectiveness and sustainability of the healthcare system. Practical implications are that there is a need for frequent policy updates and practical staff training to improve environmental management. The authors focus on the real-world implications and provide tactical recommendations for healthcare organisations that want to adopt green computing strategies successfully. A strategic perspective should be used with top management's support and all employees' involvement to achieve the organisation's future vision regarding these measures.
Shashank Mittal, Priyank Kumar Kumar Singh, Saikat Gochhait, and Shubham Kumar
IGI Global
AI is rapidly transforming the field of epidemiology. This chapter explores how AI integrates data analysis, predictive modeling, disease surveillance, and diagnostic tools to significantly improve public health outcomes. AI-driven methodologies enhance diagnostic accuracy, improve disease surveillance efficiency, and aid in developing better predictive models, all of which contribute to improved public health strategies. AI seamlessly integrates with traditional epidemiological approaches, paving the way for a new era in combating infectious diseases. Advancements in AI hold immense promise for the future of public health, with possibilities for real-time disease surveillance, personalized medicine, and more accurate predictive modeling. However, broader adoption and responsible use of AI require careful consideration of ethical issues, data privacy concerns, and collaboration among stakeholders. Ultimately, leveraging AI effectively has the potential to improve public health outcomes, ensure equitable access to healthcare, and enhance global preparedness for health crises.
Shashank Mittal, Priyank Kumar Singh, Saikat Gochhait, Nisha Gaur, and Shubham Kumar
IGI Global
Clinical trial design is undergoing a revolution fueled by artificial intelligence (AI) and translational bioinformatics. This chapter explores how AI techniques like machine learning and deep learning are being harnessed to analyze vast datasets of biological and clinical information. By integrating these insights with translational bioinformatics, researchers can identify promising drug candidates, select patients most likely to benefit from treatment, and design more efficient and targeted clinical trials. Real-world examples showcase the application of AI in immuno-oncology patient selection, drug discovery for rare diseases, predicting Alzheimer's trial outcomes, and virtual patient recruitment for cardiovascular studies. While challenges like data quality and ethical considerations exist, AI and translational bioinformatics hold immense promise for accelerating drug development, bringing life-saving therapies to patients faster.
Mohit Yadav, Priyank Kumar Singh, Saikat Gochhait, Nisha Gaur, and Puwakpitiyage Gayan Dhanushka Wijethilaka
IGI Global
This chapter explores the potential of green AI and big data informatics for personalized disease prediction in clinical decision making. Green AI prioritizes efficiency, minimizing computational resources needed to analyze vast healthcare datasets. Big data informatics provides the platform to manage and analyze these datasets for knowledge discovery. This chapter delves into how green AI algorithms optimize resource utilization while big data platforms leverage diverse patient data for more accurate, individual risk assessments. The applications in clinical decision-making encompass early detection, risk stratification, and personalized treatment plans. However, ethical considerations regarding data privacy, bias, and potential job displacement require careful attention. Finally, the future directions highlight advancements in green AI efficiency, explainable models, and integration with other health technologies, paving the way for a future of proactive healthcare and patient empowerment.
Saikat Gochhait
IGI Global
CDN is constituted of three basic components. A content provider is somebody entrusting the URI namespace of the Web objects to be dispersed. The content provider's server contains all such objects. A CDN provider can be some owner party that enables transportation conveniences to content providers to deliver content in a timely and reliable manner. They may employ geographically distributed caching and/or replica servers (surrogates or edge servers) to duplicate content. Together they may form what we call a web cluster. End users are the customers who use content from the content provider's website.
Saikat Gochhait
IGI Global
Cloud computing is gaining momentum as a subscription-oriented paradigm providing on-demand payable access to virtualized IT services and products across the net. It is a breakthrough technology that is offering on-demand access to various services across the network. Auto-scaling, though quite an attractive proposition to customers and naïve cloud service providers, has its own share of issues and challenges. This work was an attempt to classify and appreciate the auto scaling framework while outlining its challenges. Many effective and efficient auto scaling strategies are being deployed by cloud giants like Amazon AWS, Microsoft Azure, etc.
Saikat Gochhait, Yogesh Singh Rathore, Irina Leonova, Mahima Shanker Pandey, Bal Krishna Saraswat, Santosh Kumar Maurya, Hare Ram Singh, and Nidhi Bansal
Institute of Advanced Engineering and Science
<p>URL stands for uniform resource locator are the addresses of the unique resources on the internet. We all need URLs to access any type of resource on the internet, such as any web page, and document. Sometimes URLs can be long, irrelative and unattractive and unable to send sometimes via email. So, for this, we proposed a URL shortener web application based on the Python-Django platform which is fast and makes your long URLs in the shortest form which you can share on social media platforms. It makes all the messy, unattractive URLs short and shareable. Writing paper proposed a premium section in our application that gives access to the customizable URLs and analytics of your shorten URLs. Customizable URLs are the URLs you create by your own keywords. By creating a premium profile with the application, you can create your own URLs by using your own keywords. We have considered security a major part of the application that prevents the short URLs from being hacked or redirected to any advertising website or content. We store all the data related to the URL to show you the best view of your analytics and update it regularly. Main contribution in this field that for web application that provides users with a fast, secure and shortest URL for their using long URLs. Comparatively to other services that are currently available, the application provides superior security, availability, and confidentiality.</p>
RECENT SCHOLAR PUBLICATIONS
MOST CITED SCHOLAR PUBLICATIONS
GRANT DETAILS
Department of Science and Industrial Research , Govt of India with Grant of Rs 13,000,00
Ministry of Foreign Affairs, Taiwan with Grant of Rs 12,000,00
University of Deusto, Spain with Research Grant of Rs 2,000,00
University of Extremadura, Spain with Research Grant of Rs 2,000,00
Samara State Medical University, Russia with Research Visit grant of Rs 2,500,00
Symbiosis International Deemed University with Travel and Research Grant of 4,000,000
INDUSTRY EXPERIENCE
IFGL Refractories Ltd